AI/ML

Learn how to use vector search and embeddings to easily combine your data with large language models like GPT-4. You will first learn the concepts and then create three projects. ✏️ Course developed by Beau Carnes. 💻 Code: https://github.com/beaucarnes/vector-search-tutorial 🔗 Access MongoDB Atlas: https://cloud.mongodb.com/ 🏗️ MongoDB provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (01:18) What are vector embeddings? ⌨️ (02:39) What is vector search? ⌨️ (03:40) MongoDB Atlas vector search ⌨️ (04:30) Project 1: Semantic search for movie database ⌨️ (32:55) Project 2: RAG with Atlas Vector Search, LangChain, OpenAI ⌨️ (54:36) Project 3: Chatbot connected to your documentation 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

Learn what LLM as a Judge is, how it works, its benefits, challenges, and best practices for automated evaluations in AI applications.

Learn how the latest and greatest embedding models stack up against each other, as well as against some open source competition.

This llama.cpp server vision support via libmtmd pull request—via Hacker News—was merged earlier today. The PR finally adds full support for vision models to the excellent llama.cpp project. It’s documented …

GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen3 Embedding series, a new proprietary model of the Qwen model family. These models are specifically designed for text embedding, retrieval, and reranking tasks, built on the Qwen3 foundation model. Leveraging Qwen3’s robust multilingual text understanding capabilities, the series achieves state-of-the-art performance across multiple benchmarks for text embedding and reranking tasks. We have open-sourced this series of text embedding and reranking models under the Apache 2.

That gameboy blender animation...took 6 hours to render 😅. Anyway, had a ton of fun coding this up and finally getting back to some proper ML. I've been thi...

Your open source AI agent, automating engineering tasks seamlessly.

Development environments for coding agents. Enable multiple agents to work safely and independently with your preferred stack. - dagger/container-use

Turn your blind AI into a visual debugger with instant screenshot capture and analysis

New family of embedding models from Qwen, in three sizes: 0.6B, 4B, 8B - and two categories: Text Embedding and Text Reranking. The full collection can be browsed on Hugging …

Contribute to cognitivecomputations/dolphin-mcp development by creating an account on GitHub.

A collection of MCP servers. Contribute to punkpeye/awesome-mcp-servers development by creating an account on GitHub.

A brief introduction to using llamafile (a single-file tool for working with large language models) and sqlite-vec (A SQLite extension for vector search) to build a Retrival Augmentation Generation (RAG) application. This was a live online event hosted on Dec 17th 2024 in the Mozilla AI Discord, join us for the next event at at https://discord.gg/Ve7WeCJFXk LINKS: - Doc w/ links to all mentioned projects/blog posts: https://docs.google.com/document/d/17GYLzlGUyJF9EDeaa1P-dFFZnkwxATnBcg5KnNgpvPE/edit?usp=sharing - Slides: https://docs.google.com/presentation/d/14Szda-VnZzepL-1U9Nb7sXQg_TTf56OQ-KtUIMQ5xug/edit?usp=sharing

a package and makitdown plugin that transforms pdf files into markdown for llm use. - InectGit/pdf-to-markdown

Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed. - Olow304/memvid

In this video I look at the new release from Qwen of their new Embedding and Reranking models which are start of the art and most importantly open weights mo...

Most companies are using AI to cut costs. Ethan Mollick argues that the biggest mistake companies make is thinking too small. In the first episode of Strange Loop, Wharton professor and leading AI researcher Ethan Mollick joins Sana founder and CEO Joel Hellermark for a candid and wide-ranging conversation about the rapidly changing world of AI at work. They explore how AI is not just an efficiency tool but a turning point—one that forces a choice between incremental optimization and transformational scale. The discussion covers the roots of machine intelligence, the relevance of AGI, and what it takes to build organizations designed from the ground up for an AI-native future. What’s in this episode: - Why most companies are underestimating what AI makes possible - The tension between using AI for efficiency vs. scaling ambition - How traditional org charts, built for a human-only workforce, are breaking - The collapse of apprenticeship and its long-term implications - How prompting is becoming a foundational business skill - Why “cheating” with AI may be the new form of learning - The risks of using AI to optimize the past instead of inventing the future - What it means to build truly AI-native teams and organizations Strange Loop is a podcast about how artificial intelligence is reshaping the systems we live and work in. Each episode features deep, unscripted conversations with thinkers and builders reimagining intelligence, leadership, and the architectures of progress. The goal is not just to follow AI’s trajectory, but to question the assumptions guiding it. Subscribe for more conversations at the edge of AI and human knowledge. -- 00:20 - Origins: AI in the early days at MIT 01:53 - Defining and testing intelligence: Beyond the Turing test 06:35 - Redesigning organizations for the AI era 08:56 - Human augmentation or replacement 14:58 - Navigating AI's jagged frontier 17:18 - The 3 ingredients for successful AI adoption 23:31 - Roles to hire for an AI-first world 33:41 - Do orgs need a Chief AI officer? 39:45 - The interface for AI and human collaboration 43:50 - Rethinking the goals of enterprise AI 49:15 - The case for abundance 52:30 - Best and worse case scenarios 58:51 - Avoiding the trap of enterprise AI KPIs

Personal Blog

Personal Blog

A comprehensive guide outlining best practices for building reliable, user-friendly Model Context Protocol (MCP) tools with proper configuration, testing, and release management.

I run Claude Code with --dangerously-skip-permissions flag, giving it full system access. Let me show you a new way of approaching computers.

Continual learning is a huge bottleneck

Turn AI coding assistants into more reliable development partners

Field-tested practices for productive AI-assisted development. Real lessons from 6 months of daily AI pair programming, including what works, what fails, and why most engineers are doing it wrong.